Data science in agricultural economics

2022

Data on farmers’ acceptance of results-based agri-environmental schemes

Massfeller, A., Meraner, M., Huettel, S., and Uehleke, R.

Data in Brief 2022

Abs HTML

The Data presented in this article contains information on farmers’ acceptance of results-based agri-environmental schemes (AES) collected from German farmers in an online survey in spring 2020. Acceptance is measured in the willingness to participate and the intensity of participation in terms of area willing to enroll for the scheme. Personal, farm characteristics and behavioral factors have been considered. We used a between subject design to introduce a social nudge (i.e. information treatment) for one group of participants. The data was collected via the software LimeSurvey. We chose a two-step approach e.g. participants were asked for participation first and then only those who would be willing to participate were asked for the intensity in terms of area willing to enroll. This data is related to the paper Farmers’ acceptance of results-based agri-environmental schemes: a German perspective.
Farmers’ acceptance of results-based agri-environmental schemes: A German perspective

Massfeller, A., Meraner, M., Huettel, S., and Uehleke, R.

Land Use Policy 2022

Abs HTML

Results-based agri-environmental schemes (AES) seek to overcome reluctance to adopt other forms of AES in the European Union. Instead of complying with inflexible land management prescriptions, farmers receive payments after certain contracted environmental results are verified. In addition to reducing the organizational and administrative burdens, this practice allows farmers to focus on which practices can achieve the desired environmental outcomes. Farmer uptake of results-based payments may be limited by risk-adverse behavior, such as fear of not meeting environmental targets and forfeiting the contracted payment. This study investigated participation in a hypothetical results-based AES among arable farmers in the German state of North RhineWestphalia and the number of hectares enrolled (participation intensity). Our hypothetical scheme sought to foster biodiversity in pollinator and bird species by supporting weed-species richness in intensive arable production. Our split-treatment research design investigated how social nudging influences acceptance and intensity. Approximately 60% of the 63 farmers in our convenience sample expressed a willingness to participate in our hypothetical scheme. Using a Heckman style selection model, we could not detect any effect of the social nudge tested on the likelihood of participation or its intensity. Cognitive factors correlated positively with the likelihood of participation, whereas social and dispositional factors correlated with participation intensity. Perceived lack of control was the main obstacle found to adoption. The findings suggest that policies can mitigate barriers to acceptance by reducing the bureaucratic burden and being transparent over expected costs and ecosystem benefits. This study was the first to investigate farmers’ acceptance of a hypothetical results-based AES that targets the enrichment of biodiversity in arable farming and thus may serve as a stepping stone for follow-up studies.

2021

Machine learning in agricultural economics

Baylis, Kathy, Heckelei, Thomas, and Storm, Hugo

2021

Abs HTML

With the substantial growth in novel data sources and computational power, machine learning holds great potential for economic analysis. However, like any new approach, the strengths and weaknesses of these tools need to be considered when deciding where and how they can be successfully applied. In this chapter, we introduce key ML methods, from penalized regressions, to tree-based methods to neural networks, relating these approaches to common econometric practice. We then explore the potential afforded by ML to fill gaps in our current methodological toolbox. We discuss use cases like the need for flexible functional forms, the use of unstructured data, and large numbers of explanatory variables in both prediction and causal analysis. We also highlight the challenges of complex simulation models including calibration, validation and computational demands and identify places where machine learning can help. We highlight these issues drawing from existing examples in agricultural and applied economics. To unpack the “black box” of ML, we present numerous approaches used in computer science and statistics for model interpretability. Finally, we highlight some ethical issues around the use of ML. We argue that economists can play a vital role in adapting ML methods for the use in economics by combining them with our domain knowledge of economic mechanisms, and our approach to causal identification.
The case of organic dairy conversion in Norway: Assessment of multivariate neighbourhood effects

Marton, Tibor A, and Storm, Hugo

Q Open 2021

Abs HTML

This study examines the impact of neighbourhood effects and individual farm characteristics on the decision process of organic dairy conversion in Norway, using a unique, spatially explicit farm-level panel set comprising information at the population level from 2003 to 2015. Our results reveal a positive spatial spillover of neighbouring conversion, confirming previous findings. Additionally, we demonstrate that neighbouring organic dairy reversion (i.e. switching back to conventional dairy farming) and organic dairy exits (ceasing to farm altogether) exert notable negative spatial spillovers on organic conversion decisions that have not yet been shown in the literature. If organic dairy production is an important policy goal, such negative spatial spillover requires consideration within policy design and extension.
Adjusting eco-efficiency to greenhouse gas emissions targets at farm level – The case of Swedish dairy farms

Martinsson, Elin, and Hansson, Helena

Journal of Environmental Management 2021

Abs HTML

The purpose of this paper is to adjust the measure of eco-efficiency to account for specific sustainability targets at farm level. We assess eco-efficiency and adjust the scores according to a target of absolute levels of greenhouse gas (GHG) emissions, using Data Envelopment Analysis (DEA) and data from Swedish dairy farms as an illustrative example. In particular, the Swedish target of net-zero emissions in 2045 and vision of a fossil free economy are used to specify the GHG emission target used for assessing the adjusted eco-efficiency scores. We test for possible factors associated with the adjusted and unadjusted eco-efficiency using OLS-regression analysis. The study is based on data from the farm accounting data network (FADN) in year 2016 and considers the environmental pressures nutrients and contribution to global warming. Adjusted as well as unadjusted eco-efficiency scores suggest that Swedish dairy farms are highly inefficient, and that economic value added could increase by 64% (adj) or 67% (unadj) for conventional farms and by 42% (adj) or 41% (unadj) for organic farms at the same level of environmental pressure. Findings further suggest that adjusting the scores towards absolute levels of GHG emissions increases industry average efficiency. Comparing the unadjusted and adjusted efficiency scores using Spearman rank correlation indicates similar efficiency rankings between the unadjusted and adjusted scores. However, findings also indicate that adjusted and unadjusted eco-efficiency scores are associated with different influencing factors, which lends empirical support to the idea that the two types of efficiency scores are conceptually different. Policy recommendations can be made based on insights from the second stage analysis of possible influencing factors. In particular, adjusted eco-efficiency is associated with higher intensity of farming defined as output per livestock unit. Further, adjusted eco-efficiency is associated with a higher number of livestock units in conventional farms and with lower levels of labour per livestock unit in organic farms.
The cost-effectiveness of conservation auctions in the presence of asset specificity: An agent-based model

Santos, Elsa Cardona, Storm, Hugo, and Rasch, Sebastian

Land Use Policy 2021

Abs HTML

Payments for Environmental Services are a financial incentive for land users to conserve and restore ecosystems. One of the challenges in their implementation is to maximize their cost-effectiveness, or put in other words, to maximize the provision of environmental services for a given budget. This study focuses on two aspects that endanger the cost-effectiveness of such schemes: asymmetric information and asset specificity. If land users are better informed about their own provision costs, compared to the agency, they can increase their rents by demanding higher payments. The presence of asset specificity makes land users vulnerable to being harmed by opportunism. To compensate this risk, they could require higher payments or an exante compensation, likely to compromise compliance. Auctions are claimed to reduce informational rents by revealing land users’ true provision costs. However, their costeffectiveness has been shown to deteriorate if they are repeated over time because bidders can learn and adapt their strategies. Social interaction is particularly important in this context, as it allows land users to gather information on the bid cap; and it allows for trust building, which can substitute the costly formulation and enforcement of contracts, and thus reduce contracting costs. So far, there are only few studies analyzing the effect of asset specificity on the cost-effectiveness of auctions. Our study fills this gap using an agent-based model to analyze the cost-effectiveness of uniform and discriminatory one-shot and repeated auctions. In our model, land users are assumed to be embedded in a social network through which they can interact and learn. Our results suggest that repeated auctions can increase the cost-effectiveness of payments schemes in the presence of asset specificity despite of learning effects over time if land users face liquidity constraints and high time preferences.
Permanence of PES and the role of social context in the Regional Integrated Silvo-pastoral Ecosystem Management Project in Costa Rica

Rasch, Sebastian, Wünscher, Tobias, Casasola, Francisco, Ibrahim, Muhammad, and Storm, Hugo

Ecological Economics 2021

Abs HTML

We present rare, empirical evidence on the permanence of land use changes induced by a payments for ecosystem services (PES) program. A follow-up study was conducted a decade after the end of the Regional Integrated Silvo-pastoral Ecosystem Management Project (RISEMP) in Costa Rica. Econometric analysis found that silvo-pastoral practices persisted in the long term and are not reverted. On average there is also no meaningful intensification of practices after payments ceased. However, there is some heterogeneity on the individual level. We find that farms that increase adoption after the end of the project are farms with slower adoption during the project while some farms that decrease adoption are intense adopters. This indicates a pattern of convergence in the long run. Additionally, we challenge the assumption that payments are mono-causally inducing land use change by investigating non-monetary factors associated practice adoption. We find that not only PES explains adoption of silvo-pastoral practices. While it is challenging to establish clear casual linkages, we find that adoption is associated with the number of social ties to other farmers as well as negatively correlated to the exposure to traditional production paradigms measured as membership, as well as peer membership, in producer organisations.

2020

Machine learning in agricultural and applied economics

Storm, Hugo, Baylis, Kathy, and Heckelei, Thomas

European Review of Agricultural Economics 2020

Abs HTML

This review presents machine learning (ML) approaches from an applied economist’s perspective. We first introduce the key ML methods drawing connections to econometric practice. We then identify current limitations of the econometric and simulation model toolbox in applied economics and explore potential solutions afforded by ML. We dive into cases such as inflexible functional forms, unstructured data sources and large numbers of explanatory variables in both prediction and causal analysis, and highlight the challenges of complex simulation models. Finally, we argue that economists have a vital role in addressing the shortcomings of ML when used for quantitative economic analysis.
Beyond the single farm–A spatial econometric analysis of spill-overs in farm diversification in the Netherlands

Vroege, Willemijn, Meraner, Manuela, Polman, Nico, Storm, Hugo, Heijman, Wim, and Finger, Robert

Land Use Policy 2020

Abs HTML

Farm diversification is an important component of rural development and policy in Europe. We examine the influence of neighbouring farms on farm diversification decisions. Our analysis investigates spill-over effects between farms and different activities in a spatial econometric framework. Using census data from about 66,000 farms in the Netherlands, we find significant correlations of diversification activities between spatially proximate farms. These are positive for some activities, for example for nature conservation. On a local level, positive spatial dependencies between farms may result from (tacit) cooperation and information sharing within neighbourhoods. However, for other activities, such as on-farm sales, we find negative correlations on a higher spatial level, i.e. within the region, which could result from competition. Spatial aspects of cooperation and competition have important consequences for the success of policies supporting the uptake of farm diversification. Our findings thus reveal that policy measures promoting farm diversification require implementation beyond the scale of individual farms and single activities.

2019

Heterogeneous impacts of neighbouring farm size on the decision to exit: evidence from Brittany

Saint-Cyr, Legrand DF, Storm, Hugo, Heckelei, Thomas, and Piet, Laurent

European Review of Agricultural Economics 2019

Abs HTML

Accounting for spatial interactions between farms is highly relevant for the analysis of agricultural policy impacts. Existing studies, however, only account for homogeneous (average) spatial interactions. We develop a mixture modelling framework to account for unobserved heterogeneity, allowing spatial interaction to vary across endogenously defined farm types. We apply this approach to study farmer decisions to exit the farming business using a large panel dataset covering all registered farms in Brittany, France, for the period 2004–2014. While exiting is on average positively correlated with neighbouring farm size, we find substantial variation between farm types, and a negative correlation for a significant proportion of farms. The approach we develop not only enables us to identify different correlations between neighbouring farm size and exits from farming but also yields different results than pooled estimations.

2018

Reducing omitted-variable bias in spatial-interaction models by considering multiple neighbourhoods

Storm, Hugo, and Heckelei, Thomas

Spatial Economic Analysis 2018

Abs HTML

A major challenge in the analysis of micro-level spatial interaction is to distinguish actual interactions from the effects of spatially correlated omitted variables. We propose extending the simple spatially lagged explanatory (SLX) model to include two spatial weighting matrices at different spatial scales to reduce omitted-variable bias. The approach is suitable when actual interaction takes place on a smaller local level, while the omitted variables are spatially correlated at a larger regional level and correlated with the included characteristics. We provide an empirical motivation and use Monte Carlo simulation to illustrate the bias-reduction effects in certain settings.
Are maize marketers averse to quality loss in supplies? A case study from Ghana

Bassey, Nkoyo Etim, Kuhn, Arnim, and Storm, Hugo

Agricultural Economics 2018

Abs HTML

To ensure sustainable post‐harvest loss reduction, markets that are averse to quality loss and provide incentives for farmers to supply high quality produce are crucial. Such markets will be averse to quality loss, offering distinct prices and substantial rewards to farmers for the supply of quality produce. Farmers in sub‐Saharan Africa (SSA), where informal markets exist, have often assessed the rewards for the supply of quality produce as inadequate. Hence, this study investigates if intermediary buyers are actually indifferent to quality loss in supplies based on two scenarios—the informal market scenario and a hypothesized grade scenario. The analysis builds on survey data from marketers in two informal maize markets in Ghana. For the hypothesized grade scenario, random effect regression was used to examine the influence of marketer‐specific characteristics on premiums offered to farmers over different quality levels. The findings suggest that although informal markets seem not to adequately value loss reduction, investing in institutional infrastructures, such as grades and standards can change this. Furthermore, interaction among marketers and association participation positively influences the value marketers place on quality loss reduction. The result highlights the importance of standard grading systems and collaborating with market groups in minimizing quality loss.

2017

Multi-scale resilience of a communal rangeland system in South Africa

Rasch, Sebastian, Heckelei, Thomas, Storm, Hugo, Oomen, Roelof, and Naumann, Christiane

Ecological Economics 2017

Abs HTML

Resilience has either been assessed on system or individual scale so far. Ignoring the other scale may potentially change the interpretation of resilience in socio-ecological systems (SES). Thus, this paper argues that the co-evolution of both resiliencies must be studied to capture multi-scale complexity. We attempt to close this gap by assessing resilience at both scales of a village community in Thaba Nchu, South Africa. Villagers use a commonly managed rangeland for beef cattle production. An agent based model of household interaction coupled with a biophysical model of the rangeland measures the resiliencies of the SES towards a shock, a stress and a policy intervention. Currently, the SES remains in a stable attractor in terms of SES resilience. Household resilience, however, degrades in a process of structural change. A drought scenario shows improved SES resilience but structural change at household level accelerated. An increase in the number absentee herders increases the likelihood for SES collapse by eroding social embededdness. Finally, an introduced basic income grant demonstrates that the SES is able to cope with an increased number of appropriators. However, interaction of the policy intervention with an exogenous stress translates into an increased probability of SES decoupling.

2016

Bayesian estimation of non-stationary Markov models combining micro and macro data

Storm, Hugo, Heckelei, Thomas, and Mittelhammer, Ron C

European Review of Agricultural Economics 2016

Abs HTML

We develop a Bayesian framework for estimating non-stationary Markov models in situations where macro population data are available only on the proportion of individuals residing in each state, but micro-level sample data are available on observed transitions between states. Posterior distributions on non-stationary transition probabilities (TPs) are derived combining micro and macrodata using potentially asynchronous data observations, providing a new method for inferring TPs that merges previously disparate approaches. Monte Carlo simulations demonstrate how observed micro transitions can improve the precision of posterior information. We provide an empirical illustration in the context of farm structural change.

2015

Direct payments, spatial competition, and farm survival in Norway

Storm, Hugo, Mittenzwei, Klaus, and Heckelei, Thomas

American journal of agricultural economics 2015

Abs HTML

We argue that farm survival is influenced by neighboring farmers’ characteristics and, in particular, by the direct payments neighboring farmers receive. The article shows empirically that these interdependencies are crucial for an assessment of the effects of direct payments on farm survival. Using spatially explicit farm‐level data for nearly all Norwegian farms, a spatial probit model is estimated to explain farm survival from 1999 to 2009 controlling for spatial farm interdependence. We show that ignoring spatial interdependencies between farms leads to a substantial overestimation of the effects of direct payments on farm survival. To our knowledge, this article is the first attempt to empirically analyze the importance of neighboring interdependencies for the effects of direct payments on farm survival.